Don’t worry, I’m not a hippieFrom the title of this blog post, you might think that I’m going to make a touchy-feely argument about why you should respect the right of your users to do all the terrible things that every entrepreneur fears:

delete their accounts

unsubscribe from email lists

cancel their subscriptions

uninstall their apps

… but you’d be wrong.

In fact, I’m going to argue that for every early product out in the market, making it really easy to quit is completely aligned with self-interested thinking. I’ll make the assumption that all the entrepreneurs reading this post are greedy, self-interested individuals, and target the appeal straight into your dark hearts ;-)

My central argument is that if you believe that every startup is an iterative learning process that converges towards product/market fit, then you need extremely high-fidelity signals to tell you if you’re going in the right direction. That means that along with trying to charge people money from early on, which is the highest form of “I love this!” you should give people valves to tell you “I hate this!” so that you can learn more faster.

Let’s drive into this further…

Product/market fitThere’s a notion of product/market fit that Marc Andreessen references in his blog, and he calls it the “only thing that matters” and says that every startup should do everything they can to get to this point. Let’s see what he writes:

The only thing that matters is getting to product/market fit.Product/market fit means being in a good market with a product that can satisfy that market.

… and Marc continues:

Lots of startups fail before product/market fit ever happens.

My contention, in fact, is that they fail because they never get to product/market fit.

Carried a step further, I believe that the life of any startup can be divided into two parts: before product/market fit (call this “BPMF”) and after product/market fit(“APMF”).

When you are BPMF, focus obsessively on getting to product/market fit.

Do whatever is required to get to product/market fit. Including changing out people, rewriting your product, moving into a different market, telling customers no when you don’t want to, telling customers yes when you don’t want to, raising that fourth round of highly dilutive venture capital — whatever is required.

When you get right down to it, you can ignore almost everything else.

If you believe what he says, that gives you a pretty firm set of marching orders. And for early products on the market, getting to to this point in which your product is good enough and the market is compelling enough is a tough slog. So the question is, how do you navigate your way to product/market fit?

At the heart of every startup is a learning loopFor the idea that every startup is inherently a learning machine, we can turn to two of my favorite startup people, Steve Blank and Eric Ries. Eric has bloggedin a lotof detail about how he believes that inside of every startup is an OODA loop that involves trying stuff out, learning, and trying more stuff again. And of course a lot of these ideas are built off of Steve Blank’s Customer Development framework that I’d encourage my readers to look into as well.

In this light, to combine the two ideas: Every startup is a series of iterative experiments that gets you from zero to product/market fit, and if you can do it before running out of money, then you might get rich ;-)

And the decision-making process in this approach is totally different. In most product strategy conversations I’ve been involved in, the most heated debates center around whether a particular product will work, and all the pros and cons of the situation. Contrast this to a learning-centric approach, which emphasizes whether or not experimenting with an idea will yield insights, and how much it’ll cost to learn these insights.

In other words, you’re much more likely to try things that will fail, if those failures teach you something important about the market.

Of course, all of the decisions that power these iterations rely data – and the better the data, the better your decisions will be, naturally. So where do you get the data to tell you if customers are happy or not about your product?

Explicit signals beat implicit signals almost every timeOne of the key lessons I took away from my time from the behavioral targeting ad industry is that explicit data is much, much better than implicit data, when it comes to predicting user behavior.

That is, you’d prefer explicit “intent” data like:

made a purchase

used a student loan calculator

searched for “palo alto bmw dealership”

filled out a form

versus the less valuable implicit “interest” data like:

have similar demographics to other people who buy

visit the same publications as similar customers

having a pattern of reading finance articles

So if you are looking to collect data to drive decisions, then the best kind comes from the explicit data of having users specifically take action, whether it’s positive or negative. Purchase intent data, as illustrated above, is positive – and quitting intent gives you the negative half. In fact, if you only look at the positive feedback, you might be ignoring 50% of your data.

As a result, you want lots of explicit data points in the axis of “I love it!” to “I hate it!” which includes people giving you money (maybe donations being the ultimate form of love) to allowing them to easily quit. Make it easy for your users to quit, unsubscribe, or otherwise cancel – it gives you the strong signal when you’re doing wrong! And make sure to track it and include it in all of your quantitative experiments as well.